Spam Email Classification Using Machine Learning Algorithms
S Kranthi Reddy1, P Balaji Tarun2, S.Rushika3, P. Deekshith Reddy4, E.Anjala5
1S Kranthi Reddy, Assistant Professor, Department of Computer Science & Engineering, Vignan Institute of Technology and Science, Deshmukhi, Yadadri Bhuvanagiri (Telangana), India.
2P Balaji Tarun, B.Tech, Department of (CSE), Vignan Institute of Technology and Science, Deshmukhi, Yadadri Bhuvanagiri (Telangana), India.
3S.Rushika, B.Tech, Department of (CSE), Vignan Institute of Technology and Science, Deshmukhi, Yadadri Bhuvanagiri (Telangana), India.
4P. Deekshith Reddy, B.Tech, Department of (CSE), Vignan Institute of Technology and Science, Deshmukhi, Yadadri Bhuvanagiri (Telangana), India.
5E.Anjala, B.Tech, Department of (CSE), Vignan Institute of Technology and Science, Deshmukhi, Yadadri Bhuvanagiri (Telangana), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1748-1752 | Volume-8 Issue-7, May 2019 | Retrieval Number: F3919048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Email is a standout amongst the most secure vehicle for online correspondence and exchanging information or messages through the web. A congesting increment in fame, the quantity of spontaneous information has additionally expanded quickly. To channel information, diverse methodologies exist which consequently identify and expel these indefensible messages. As spam messages are making bother everybody, Machine Learning Techniques now days used to consequently channel the spam email in an effective rate. This paper audits the execution of support vector machines, decision trees and logistic regression on Spam Email information. These three algorithms were tried on an ongoing dataset, where the dimensionality of spam messages were more than 5000 and SVM performed best when utilizing linear kernel. Logistic Regression and SVM’s had worthy test execution regarding accuracy and speed. Be that as it may, SVM’s had strikingly more accuracy.
Keyword: Spam Email, Decision Trees, Logistic Regression, Support Vector Machines.
Scope of the Article: Classification.